Multivariate Analysis of Seed Chemical Diversity among Jatropha curcas in Botswana

Jatropha (Jatropha curcas L.) has been identified as a potential bioenergy feedstock in arid regions, but knowledge of the diversity of its chemical characteristics is limited. In this study, 61 Jatropha accessions growing in Botswana, where both severe drought and winter frosts frequently occur, were analyzed for their seed chemical properties. Histogram analyses and meta-analysis comparisons with seeds from other countries/continents showed that the median/mean dry seed weight, toxic compound phorbol esters, and C18:0 fatty acid levels in the Botswanan accessions were lower than those from other countries/continents. A clustered heat map analysis indicated five clades for the Botswanan accessions, and their physicochemical traits were also categorized into five groups. Many positive and negative correlations were observed among the chemical traits, including negative correlations between the C18:3 (linolenic acid) content and yield-related traits (lipid content and dry seed weight). Principal component analysis highlighted the existence of accessions with highly deviated seed chemical compositions, such as those enriched in C18:0/C18:1 and C16:0/C16:1/C18:3 fatty acids. Overall, the present study suggests considerable diversity in the seed chemical compositions of Botswanan Jatropha accessions. Various accessions could be useful as feedstock for specific industrial products, as well as for breeding materials for the fortification of specific chemical ingredients

Probing Differential Metabolome Responses among Wheat Genotypes to Heat Stress Using Fourier Transform Infrared-Based Chemical Fingerprinting

Heat stress is one of the major environmental constraints for wheat production; thus, a comprehensive understanding of the metabolomic responses of wheat is required for breeding heat-tolerant varieties. In this study, the metabolome responses of heat-tolerant genotypes Imam and Norin 61, and susceptible genotype Chinese Spring were comparatively analyzed using Fourier transform infrared (FTIR) spectroscopy in combination with chemometric data mining techniques. Principal component analysis of the FTIR data suggested a spectral feature partially overlapping between the three genotypes. FTIR spectral biomarker assay showed similar heat responses between the genotypes for markers Fm482 and Fm1502, whereas genotype-dependent variations were observed for other markers. The markers Fm1251 and Fm1729 showed contrasting behaviors between heat-tolerant and susceptible genotypes, suggesting that these markers may potentially serve as a tool for distinguishing heat-tolerant genotypes. Linear discriminant analysis (LDA) of the spectra demonstrated a clear separation between the three genotypes in terms of the heat stress effect. Analysis of LDA coefficients identified several spectral regions that were potentially responsible for the discrimination of FTIR spectra between different genotypes and environments. These results suggest that a combination of FTIR and chemometrics can be a useful technique for characterizing the metabolic behavior of diverse wheat genotypes under heat stress

Chemical Fingerprinting of Heat Stress Responses in the Leaves of Common Wheat by Fourier Transform Infrared Spectroscopy

Wheat (Triticum aestivum L.) is known to be negatively affected by heat stress, and its production is threatened by global warming, particularly in arid regions. Thus, efforts to better understand the molecular responses of wheat to heat stress are required. In the present study, Fourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress. Wheat plants at the three-leaf stage were subjected to heat stress at a 42 °C daily maximum temperature for 3 days, and this led to delayed growth in comparison to that of the control. Measurement of FTIR spectra and their principal component analysis showed partially overlapping features between heat-stressed and control leaves. In contrast, supervised machine learning through linear discriminant analysis (LDA) of the spectra demonstrated clear discrimination of heat-stressed leaves from the controls. Analysis of LDA loading suggested that several wavenumbers in the fingerprinting region (400–1800 cm−1) contributed significantly to their discrimination. Novel spectrum-based biomarkers were developed using these discriminative wavenumbers that enabled the successful diagnosis of heat-stressed leaves. Overall, these observations demonstrate the versatility of FTIR-based chemical fingerprints for use in heat-stress profiling in wheat